Generating Uniform Random Vectors

In this paper, we study the rate of convergence of the Markov chain X _n+1=AX _n +b _n mod p, where A is an integer matrix with nonzero integer eigenvalues and {b _n } _n is a sequence of independent and identically distributed integer vectors. If _i±1 for all eigenvalues _i of A, then n=O((log p)²) steps are sufficient and n=O(log p) steps are necessary to have X _n sampling from a nearly uniform distribution. Conversely, if A has the eigenvalue ₁=±1, and _i±1 for all i1, n=O(p²) steps are necessary and sufficient.     

Supermodular Comparison of Time-to-Ruin Random Vectors

This paper studies time-to-ruin random vectors for multivariate risk processes. Two cases are considered: risk processes with independent increments and risk processes evolving in a common random environment (e.g., because they share the same economic conditions). As expected, increasing the dependence between the risk processes increases the dependence between their respective time-to-ruin random variables. This article is dedicated to the memory of our beloved friend Benjamin Zeev Levikson who passed away on July 16, 2005.     

Approximation of Projections of Random Vectors

Let X be a d-dimensional random vector and X _θ its projection onto the span of a set of orthonormal vectors {θ ₁,…,θ _k }. Conditions on the distribution of X are given such that if θ is chosen according to Haar measure on the Stiefel manifold, the bounded-Lipschitz distance from X _θ to a Gaussian distribution is concentrated at its expectation; furthermore, an explicit bound is given for the expected distance, in terms of d, k, and the distribution of X, allowing consideration not just of fixed k but of k growing with d. The results are applied in the setting of projection pursuit, showing that most k-dimensional projections of n data points in ℝ ^d are close to Gaussian, when n and d are large and k=clog (d) for a small constant c.     

Common Principal Components for Dependent Random Vectors

Let the kp-variate random vector X be partitioned into k subvectors X_i of dimension p each, and let the covariance matrix Ψ of X be partitioned analogously into submatrices Ψ_ij. The common principal component (CPC) model for dependent random vectors assumes the existence of an orthogonal p by p matrix β such that β^tΨ_ijβ is diagonal for all (i, j). After a formal definition of the model, normal theory maximum likelihood estimators are obtained. The asymptotic theory for the estimated orthogonal matrix is derived by a new technique of choosing proper subsets of functionally independent parameters.     

Convex Ordering for Random Vectors using Predictable Representation

We prove convex ordering results for random vectors admitting a predictable representation in terms of a Brownian motion and a non-necessarily independent jump component. Our method uses forward-backward stochastic calculus and extends the results proved in Klein et al. (Electron J Probab 11(20):27, 2006) in the one-dimensional case. We also study a geometric interpretation of convex ordering for discrete measures in connection with the conditions set on the jump heights and intensities of the considered processes. The work described in this paper was partially supported by a grant from City University of Hong Kong (Project No. 7200108).     

Sample Covariance Matrix for Random Vectors with Heavy Tails

We compute the asymptotic distribution of the sample covariance matrix for independent and identically distributed random vectors with regularly varying tails. If the tails of the random vectors are sufficiently heavy so that the fourth moments do not exist, then the sample covariance matrix is asymptotically operator stable as a random element of the vector space of symmetric matrices.     

随机向量独立性判别准则及其应用

介绍了随机向量独立性的三种常用判别准则 ,提出了相应的教学法的建议 .     

The Central Limit Problem for Random Vectors with Symmetries

Motivated by the central limit problem for convex bodies, we study normal approximation of linear functionals of high-dimensional random vectors with various types of symmetries. In particular, we obtain results for distributions which are coordinatewise symmetric, uniform in a regular simplex, or spherically symmetric. Our proofs are based on Stein’s method of exchangeable pairs; as far as we know, this approach has not previously been used in convex geometry. The spherically symmetric case is treated by a variation of Stein’s method which is adapted for continuous symmetries. This work was done while at Stanford University.     

Characterizations of the Random Order Values by Harsanyi Payoff Vectors

A Harsanyi payoff vector (see Vasil’ev in Optimizacija Vyp 21:30–35, 1978) of a cooperative game with transferable utilities is obtained by some distribution of the Harsanyi dividends of all coalitions among its members. Examples of Harsanyi payoff vectors are the marginal contribution vectors. The random order values (see Weber in The Shapley value, essays in honor of L.S. Shapley, Cambridge University Press, Cambridge, 1988) being the convex combinations of the marginal contribution vectors, are therefore elements of the Harsanyi set, which refers to the set of all Harsanyi payoff vectors.The aim of this paper is to provide two characterizations of the set of all sharing systems of the dividends whose associated Harsanyi payoff vectors are random order values. The first characterization yields the extreme points of this set of sharing systems and is based on a combinatorial result recently published (Vasil’ev in Discretnyi Analiz i Issledovaniye Operatsyi 10:17–55, 2003) the second characterization says that a Harsanyi payoff vector is a random order value iff the sharing system is strong monotonic.This work was partly done whilst Valeri Vasil’ev was visiting the Department of Econometrics at the Free University, Amsterdam. Financial support from the Netherlands Organisation for Scientific Research (NWO) in the framework of the Russian-Dutch programme for scientific cooperation, is gratefully acknowledged. The third author would also like to acknowledge partial financial support from the Russian Fund of Basic Research (grants 98-01-00664 and 00-15-98884) and the Russian Humanitarian Scientific Fund (grant 02-02-00189a).     

Some Comments on Deviation Inequalities for Infinitely Divisible Random Vectors

In this paper, we consider deviation inequalities for infinitely divisible random vectors in R ^k and infinite-dimensional spaces l _p, 1 p We compare the results obtained using the covariance representation for infinitely divisible random vectors with the well-known Talagrand's result on measure concentration phenomenon.     

φ-混合重尾随机向量序列的Chover型重对数律

本文讨论同分布的φ-混合随机向量序列其共同分布属于某个没有Gauss分量的广义的半稳定律的吸引场部分和的积分检验的极限结果,由此可推出相应的Chover型重对数律.     

配备FGM Copulas二维随机向量之和的相依性

本文研究了配备Farlie-Gumbel-Morgenstern Copulas的二维随机向量之和的相依性,得到了在这类Copulas函数下两个独立的随机向量之和的Kendall及Spearman相依系数的一般公式;并针对边缘分布分别为指数分布的情况推导出了具体的公式;证明了当边缘分布满足一定的条件时,不存在尾部相依性.此外,对于几种不同边缘分布的情况进行了随机模拟与比较.这些方法及结果对两个企业(公司)合并后某两个随机指标之间的相依性问题的研究具有理论指导意义,为这类问题的进一步探索提供了理论基础.     

The Almost Sure Limit Theorem for Sums of Random Vectors

We establish a sufficient condition for an almost sure limit theorem for sums of independent random vectors under minimal moment conditions and assumptions on normalizing sequences. We provide an example showing that our condition is close to the optimal one, as well as a related sufficient condition due to Berkes and Dehling. Bibliography: 5 titles.     

A Multivariate CLT for Decomposable Random Vectors with Finite Second Moments

Stein's method is used to derive a CLT for dependent random vectors possessing the dependence structure from Barbour et al. J. Combin. Theory Ser. B 47, 125–145, but under the assumption of second moments only. This allows us to derive Lindeberg–Feller type theorems for sums of random vectors with certain dependence structures. We apply the main theorem to the study of three problems: local dependence, random graph degree statistics and finite population statistics. In particular, we consider U-statistics of independent observations as well as of observations drawn without replacement.     

Ordering Functions of Random Vectors, with Application to Partial Sums

It is known that the sums of the components of two random vectors (X ₁,X ₂,…,X _n ) and (Y ₁,Y ₂,…,Y _n ) ordered in the multivariate (s ₁,s ₂,…,s _n )-increasing convex order are ordered in the univariate (s ₁+s ₂+?+s _n )-increasing convex order. More generally, real-valued functions of (X ₁,X ₂,…,X _n ) and (Y ₁,Y ₂,…,Y _n ) are ordered in the same sense as long as these functions possess some specified non-negative cross-derivatives. This note extends these results to multivariate functions. In particular, we consider vectors of partial sums (S ₁,S ₂,…,S _n ) and (T ₁,T ₂,…,T _n ) where S _j =X ₁+?+X _j and T _j =Y ₁+?+Y _j and we show that these random vectors are ordered in the multivariate (s ₁,s ₁+s ₂,…,s ₁+?+s _n )-increasing convex order. The consequences of these general results for the upper orthant order and the orthant convex order are discussed.     

模糊随机向量凸集的结构和性质

在模糊随机向量凸集概念的基础上,分析了模糊随机向量凸集的结构,研究了模糊随机向量凸集的基本性质.     

Symmetrical Independence Tests for Two Random Vectors with Arbitrary Dimensional Graphs

Test of independence between random vectors X and Y is an essential task in statistical inference.One type of testing methods is based on the minimal spanning t...     

Some Convergence Properties for Weighted Sums of Martingale Difference Random Vectors

Let {X_ni,F_ni;1≤i≤n,n≥1} be an array of R^d martingale difference random vectors and {A_ni,1≤i≤n,n≥1} be an array of m×d matrices of real numbers.In this paper,the Marcinkiewicz-Zygmund type weak law of large numbers for maximal weighted sums of martingale difference random vectors is obtained with not necessarily finite p-th(1相似文献